dailyai.report

The GRASP planner optimizes long-horizon trajectories by lifting them into virtual states. This architecture allows for parallel optimization across time and integrates stochasticity directly into the process. Researchers at Berkeley AI Research developed the system to make gradient-based planning practical for complex dynamics. It reduces the computational overhead typically found in world model planning.